def parent_reproduction(parent1, parent2, nodes):
res = []
res.extend(parent1)
for e2 in parent2:
found = False
for e1 in parent1:
if (e2.same(e1)):
found = True
break
if (not found):
res.append(e2)
a, b = util.cic(res)
while (a):
b.sort(key=lambda x: x.cost)
if (len(b) > 2):
el = b[random.randint(len(b) - 3, len(b) - 1)]
elif (len(b) == 2):
el = b[random.randint(len(b) - 2, len(b) - 1)]
else:
el = b[0]
util.pop_element(res, el)
a, b = util.cic(res)
return res
def kruskal(all_edge):
res = []
all_edge.sort(key=lambda x: x.cost)
for e in all_edge:
res.append(e)
a, b = util.cic(res)
if (a):
res.pop()
return res
def tabu(arr_edge,all_edge,nodes,tabu_list): free_edge = [] solution_array = [] for e_all in all_edge: find = False for edge in arr_edge: if(edge.same(e_all)): find = True break if(not find): e = e_all.copy() free_edge.append(e) for e in free_edge: arr_edge.append(e) a,b = util.cic(arr_edge) for h in b: if(h.same(e)): continue elif(in_list(h,tabu_list)): continue else: util.pop_element(arr_edge,h) newmin = util.blabla(nodes,arr_edge) x,y = util.copy_array(arr_edge,newmin) solution_array.append((x,y,h.copy())) arr_edge.append(h) util.pop_element(arr_edge,e) minarr,mincost,tabuedge = solution_array[0] #return((minarr,mincost)) counter =0 minpos =0 for a,c,e in solution_array[1:]: counter +=1 if(c < mincost): minpos = counter mincost = c minarr,mincost,tabuedge = solution_array[minpos] append_to_tabu(tabuedge,tabu_list) return((minarr,mincost))
def grasp(all_edge):
temp_arr, useless = util.copy_array(all_edge, 0)
res = []
temp_arr.sort(key=lambda x: x.cost)
while (True):
if (0 == (len(temp_arr) - 2)):
element = temp_arr[random.randint(0, 1)]
elif (0 == (len(temp_arr) - 1)):
element = temp_arr[0]
else:
element = temp_arr[random.randint(0, (RNJESUS - 1))]
util.pop_element(temp_arr, element)
res.append(element)
a, b = util.cic(res)
if (a):
res.pop()
if (len(temp_arr) == 0):
break
return res
def parent_reproduction(parent1, parent2, nodes):
res = []
res.extend(parent1)
for e2 in parent2:
found = False
for e1 in parent1:
if (e2.same(e1)):
found = True
break
if (not found):
res.append(e2)
a, b = cc.cic(res)
while (a):
res.sort(key=lambda x: x.cost)
res.pop(random.randint(len(res) - 2, len(res) - 1))
a, b = util.cic(res)
return res
def heuristic(arr_edge,base_cost,all_edge,nodes): solution_array = [] free_edge =[] for e_all in all_edge: find = False for edge in arr_edge: if(edge.same(e_all)): find = True break if(not find): e = e_all.copy() free_edge.append(e) for e in free_edge: arr_edge.append(e) a,b = util.cic(arr_edge) for h in b: if(h.same(e)): continue else: util.pop_element(arr_edge,h) newmin = util.blabla(nodes,arr_edge) solution_array.append(util.copy_array(arr_edge,newmin)) arr_edge.append(h) util.pop_element(arr_edge,e) solution_array.append((arr_edge,base_cost)) z,mincost = solution_array[0] minpos = 0 i=0 for a,c in solution_array: i+=1 if(mincost > c): minpos = i z,mincost = a,c return((z,mincost))